All Algorithms implemented in Python

🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.

ChatGLM-6B: An Open Bilingual Dialogue Language Model | 开源双语对话语言模型

A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training

中文LLaMA&Alpaca大语言模型+本地CPU/GPU训练部署 (Chinese LLaMA & Alpaca LLMs)

A framework for training and evaluating AI models on a variety of openly available dialogue datasets.

Pre-Training with Whole Word Masking for Chinese BERT（中文BERT-wwm系列模型）

Accessible large language models via k-bit quantization for PyTorch.

The official repository for ERNIE 4.5 and ERNIEKit – its industrial-grade development toolkit based on PaddlePaddle.

Implementation of Graph Convolutional Networks in TensorFlow

An open source library for deep learning end-to-end dialog systems and chatbots.

Code for the paper "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer"

XLNet: Generalized Autoregressive Pretraining for Language Understanding

中文 NLP 预处理、解析工具包，准确、高效、易用 A Chinese NLP Preprocessing & Parsing Package www.jionlp.com

[EMNLP 2021] SimCSE: Simple Contrastive Learning of Sentence Embeddings https://arxiv.org/abs/2104.08821

Pretrained language model and its related optimization techniques developed by Huawei Noah's Ark Lab.

⚡LLM Zoo is a project that provides data, models, and evaluation benchmark for large language models.⚡

Code for the ICLR 2023 paper "GPTQ: Accurate Post-training Quantization of Generative Pretrained Transformers".

Official Implementation of EAGLE-1 (ICML'24), EAGLE-2 (EMNLP'24), and EAGLE-3 (NeurIPS'25).

Underthesea - Vietnamese NLP Toolkit

[ICML 2023] SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models

some attention implements

Pytorch-Named-Entity-Recognition-with-BERT

Implementation of RETRO, Deepmind's Retrieval based Attention net, in Pytorch

A Keras TensorFlow 2.0 implementation of BERT, ALBERT and adapter-BERT.

CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Generation

ChineseNER based on BERT, with BiLSTM+CRF layer

Mem2Seq: Effectively Incorporating Knowledge Bases into End-to-End Task-Oriented Dialog Systems

基于seq2seq模型的简单对话系统的tf实现，具有embedding、attention、beam_search等功能，数据集是Cornell Movie Dialogs

🐝Tensorflow Implementation of Spatial Transformer Networks